Scalable NoC Integration Strategies for AI and Data Center Accelerators

  • Authors

    • VenkataKrishna Brahmam Pogadadanda Senior RTL Design Engineer at AMD-Xilinx, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2024PII4X8N

    Published 11-07-2024

  • Network-on-Chip (NoC), AI Accelerators, Data Center Architectures, Scalability, Heterogeneous Computing, Chiplet Integration, Interconnect Optimization, Low-Latency Communication, Energy Efficiency, Parallel Processing

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

  • Abstract

    Scalable Network-on-Chip (NoC) integration methods are crucial for improving the performance and efficiency of today's AI accelerators and data center architectures that use heterogeneous computing systems requiring high-speed and low-latency communication among processors. With AI workloads becoming more complex, traditional interconnect systems are struggling with the problems of scalability, insufficient bandwidth, high communication overhead, heat management, and power efficiency. NoC designs solve these problems by offering structured, packet-based communication systems that not only enhance data transfer efficiency but also allow for parallel processing in multicore and manycore accelerator systems. This paper discusses different scalable NoC integration methods, such as hierarchical topologies, adaptive routing algorithms, chiplet-based interconnects, and energy-efficient communication models, which are specially designed for AI and data center accelerators. Our approach is to study the existing NoC architectures, measure the performance parameters that include latency, throughput, energy consumption, and thermal behavior, and perform a case study based on accelerator-centric data center environments. The results would point to the best NoC design solutions that not only offer a good balance between scalability and efficiency but also minimize congestion and communication bottlenecks. The main findings discuss how making routing more intelligent, integrating modules in a simple way, and optimizing with thermal-awareness can help maintain the performance of accelerators over the long term. On the other hand, future perspectives focus on how advanced NoC technologies can shape the next generation of computing infrastructures driven by AI.

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